One of the major use cases for AI is sentiment analysis, which uses natural language processing (NLP) to gain insight into how a business is seen on social media. According to

“Natural language refers to language that is spoken and written by people, and natural language processing (NLP) attempts to extract information from the spoken and written word using algorithms. NLP encompasses active and a [sic] passive modes: natural language generation (NLG), or the ability to formulate phrases that humans might emit, and natural language understanding (NLU), or the ability to build a comprehension of a phrase, what the words in the phrase refer to, and its intent. In a conversational system, NLU and NLG alternate, as algorithms parse and comprehend a natural-language statement and formulate a satisfactory response to it.” 

In their article How Artificial Intelligence and Machine Learning Can Impact Market Design, Paul R. Milgrom and Steve Tadelis give some interesting use cases for NLP, Airbnb, along with many others have seen exponential growth since their inception because they provide “businesses and individuals with previously unavailable opportunities to purchase or profit from online trading.” Besides the new marketplaces created for these wholesalers and retailers, “the so called ‘gig economy’ is comprised of marketplaces that allow individuals to share their time or assets across different productive activities and earn extra income,” according to Milgrom and Tadelis.

Another important area for AI is text analytics. In his article Text Analytics: How to Analyse and Mine Words and Natural Language in BusinesseBernard Marr states that, “Text analytics, also known as text mining, is a process of extracting value from large quantities of unstructured text data.” Marr explains that, “While the text itself is structured to make sense to a human being (i.e., A company report split into sensible sections) it is unstructured from an analytics perspective because it doesn’t fit neatly into a relational database or rows and columns of a spreadsheet. Traditionally, the only structured part of text was the name of the document, the date it was created and who created it.” “Access to huge text data sets and improved technical capability means text can be analysed to extract high-quality information above and beyond what the document actually says,” Marr argues. “Text can be assessed for commercially relevant patterns such as an increase or decrease in positive feedback from customers, or new insights that could lead to product tweaks, etc.” This means text analytics can help us discover things we didn’t already know but, perhaps more importantly, had no way of previously knowing. These could be incredibly important insights for a business both about itself and, potentially, about its competitors.

Marr says that, “Text analytics is particularly useful for information retrieval, pattern recognition, tagging and annotation, information extraction, sentiment assessment and predictive analytics.” It could both reveal what customers think about a company’s products or services, or highlight the most common issues that instigate customer complaints.